Upstage
Upstage is a leading artificial intelligence (AI) company specializing in delivering above-human-grade performance LLM components.
Solar LLMβ
Solar Mini Chat is a fast yet powerful advanced large language model focusing on English and Korean. It has been specifically fine-tuned for multi-turn chat purposes, showing enhanced performance across a wide range of natural language processing tasks, like multi-turn conversation or tasks that require an understanding of long contexts, such as RAG (Retrieval-Augmented Generation), compared to other models of a similar size. This fine-tuning equips it with the ability to handle longer conversations more effectively, making it particularly adept for interactive applications.
Other than Solar, Upstage also offers features for real-world RAG (retrieval-augmented generation), such as Groundedness Check and Layout Analysis.
Installation and Setupβ
Install langchain-upstage
package:
pip install -qU langchain-core langchain-upstage
Get API Keys and set environment variable UPSTAGE_API_KEY
.
Upstage LangChain integrationsβ
API | Description | Import | Example usage |
---|---|---|---|
Chat | Build assistants using Solar Mini Chat | from langchain_upstage import ChatUpstage | Go |
Text Embedding | Embed strings to vectors | from langchain_upstage import UpstageEmbeddings | Go |
Groundedness Check | Verify groundedness of assistant's response | from langchain_upstage import UpstageGroundednessCheck | Go |
Layout Analysis | Serialize documents with tables and figures | from langchain_upstage import UpstageLayoutAnalysisLoader | Go |
See documentations for more details about the features.
Quick Examplesβ
Environment Setupβ
import os
os.environ["UPSTAGE_API_KEY"] = "YOUR_API_KEY"
Chatβ
from langchain_upstage import ChatUpstage
chat = ChatUpstage()
response = chat.invoke("Hello, how are you?")
print(response)
API Reference:
Text embeddingβ
from langchain_upstage import UpstageEmbeddings
embeddings = UpstageEmbeddings()
doc_result = embeddings.embed_documents(
["Sam is a teacher.", "This is another document"]
)
print(doc_result)
query_result = embeddings.embed_query("What does Sam do?")
print(query_result)
API Reference:
Groundedness Checkβ
from langchain_upstage import UpstageGroundednessCheck
groundedness_check = UpstageGroundednessCheck()
request_input = {
"context": "Mauna Kea is an inactive volcano on the island of Hawaii. Its peak is 4,207.3 m above sea level, making it the highest point in Hawaii and second-highest peak of an island on Earth.",
"answer": "Mauna Kea is 5,207.3 meters tall.",
}
response = groundedness_check.invoke(request_input)
print(response)
API Reference:
Layout Analysisβ
from langchain_upstage import UpstageLayoutAnalysisLoader
file_path = "/PATH/TO/YOUR/FILE.pdf"
layzer = UpstageLayoutAnalysisLoader(file_path, split="page")
# For improved memory efficiency, consider using the lazy_load method to load documents page by page.
docs = layzer.load() # or layzer.lazy_load()
for doc in docs[:3]:
print(doc)